The present invention relates generally to artificial neuron apparatus, and more particularly to artificial neurons based on resistive memory cells.
Neuromorphic technology relates to computing systems which are inspired by biological architectures of the nervous system. Conventional computing systems are based on binary logic and sequential von Neumann architecture. While efficient in performing tasks such as numerical calculations, separation between external memory and processors in these systems leads to energy-hungry data movements. Compared to the human brain, the conventional computing paradigm is highly inefficient in terms of power consumption and space requirements. These issues have prompted a significant research effort to understand the highly efficient computational paradigm of the human brain and to create artificial cognitive systems with unprecedented computing power. For example, there is a strategic intent to develop “neuromorphic co-processors” able to carry out event-based computations in compute-intensive tasks such as “big data” analytics and real-world sensory applications.
Neurons, along with synapses, are basic computational units in the brain. In biological neurons, a thin lipid-bilayer membrane is used to separate the electrical charge inside of the cell from that outside of it. A vital function of the neuron is the update of the membrane potential which represents the stored neuron state. The membrane potential is modified by the arrival of neuron input signals. When the membrane potential traverses a specific voltage threshold, the neuron will “fire,” generating an output signal known as an “action potential” or “spike,” and then revert to its initial state. These spikes are conveyed to other neurons via synapses which change their connection strength (“plasticity” or “synaptic weight”) as a result of neuronal activity.
The realization of efficient artificial neurons is of fundamental importance to neuromorphic technology. Most prior proposals for artificial neurons are based on hybrid analog/digital VLSI circuits, requiring complex CMOS circuitry with a large number of transistors to emulate neuronal functionality. Resistive memory cells such as phase-change memory (PCM) cells have also been recognized as suitable candidates for the realization of neural hardware (see, e.g., “The Ovonic Cognitive Computer—A New Paradigm,” Ovshinsky, Proc. E/PCOS, 2004, and “Novel Applications Possibilities for Phase-Change Materials and Devices,” Wright et al., Proc. E/PCOS, 2013). Resistive memory cells are programmable-resistance devices which rely on the variable resistance characteristics of a volume of resistive material disposed between a pair of electrodes. These cells are memristors, i.e., devices that remember the history of the current that has flowed through them. A neuron circuit based on Mott memristors has also been proposed in “A Scalable Neuristor built with Mott Memristors,” Pickett et al. Nature Materials, 2013.
Prior artificial neurons based on memristive devices have been concerned only with emulating the integrate-and-fire functionality or emulating the biological action potential shape. Concrete realizations for efficient artificial neurons for operation in a neural network configuration remain a challenge. Moreover, in real neuronal networks the updates to neuron membrane potentials can be of an excitatory nature (increase of the membrane potential) or an inhibitory nature (decrease of the membrane potential). Artificial neurons able to meet such requirements would be highly desirable.